Consistent Mesh Diffusion

• URL: http://arxiv.org/abs/2312.00971v1
• Date: Fri, 1 Dec 2023 23:25:14 GMT
• Title: Consistent Mesh Diffusion
• Authors: Julian Knodt and Xifeng Gao
• Abstract summary: Given a 3D mesh with a UV parameterization, we introduce a novel approach to generating textures from text prompts. We demonstrate our approach on a dataset containing 30 meshes, taking approximately 5 minutes per mesh.
• Score: 8.318075237885857
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Given a 3D mesh with a UV parameterization, we introduce a novel approach to generating textures from text prompts. While prior work uses optimization from Text-to-Image Diffusion models to generate textures and geometry, this is slow and requires significant compute resources. Alternatively, there are projection based approaches that use the same Text-to-Image models that paint images onto a mesh, but lack consistency at different viewing angles, we propose a method that uses a single Depth-to-Image diffusion network, and generates a single consistent texture when rendered on the 3D surface by first unifying multiple 2D image's diffusion paths, and hoisting that to 3D with MultiDiffusion~\cite{multidiffusion}. We demonstrate our approach on a dataset containing 30 meshes, taking approximately 5 minutes per mesh. To evaluate the quality of our approach, we use CLIP-score~\cite{clipscore} and Frechet Inception Distance (FID)~\cite{frechet} to evaluate the quality of the rendering, and show our improvement over prior work.

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